Managing meeting data

ABSTRACT

A device may receive meeting data associated with a first meeting, the first meeting having previously occurred. The device may obtain, based on the meeting data, data identifying at least one individual associated with the first meeting and data identifying at least one topic associated with the first meeting. In addition, the device may identify a second meeting based on the at least one individual or the at least one topic, the second meeting having not yet occurred. The device may provide, to a user device associated with the second meeting and based on identifying the second meeting, at least a portion of the meeting data associated with the first meeting.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/289,323, filed Feb. 28, 2019 (now U.S. Pat. No. 10,536,289), which isa continuation of U.S. patent application Ser. No. 16/116,600, filedAug. 29, 2018 (now U.S. Pat. No. 10,263,799), which are incorporatedherein by reference.

BACKGROUND

Meetings between individuals often take place using a variety oftelecommunications devices and for a variety of purposes. Meetings mayinvolve a variety of different individuals and span a variety of topics.

SUMMARY

According to some implementations, a method may comprise: receiving, bya device, meeting data associated with a first meeting, the firstmeeting having previously occurred; obtaining, by the device and basedon the meeting data, data identifying at least one individual associatedwith the first meeting; obtaining, by the device and based on themeeting data, data identifying at least one topic associated with thefirst meeting; identifying, by the device, a second meeting based on theat least one individual or the at least one topic, the second meetinghaving not yet occurred; and providing, by the device and to a userdevice associated with the second meeting and based on identifying thesecond meeting, at least a portion of the meeting data associated withthe first meeting.

According to some implementations, a device may comprise: one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive first meeting data associatedwith a first meeting, the first meeting having not yet occurred; obtain,based on the first meeting data, data identifying at least oneindividual associated with the first meeting; obtain, based on the firstmeeting data, data identifying at least one topic associated with thefirst meeting; provide, to a meeting relevance model, the dataidentifying the at least one individual associated with the firstmeeting and the data identifying at least one topic associated with thefirst meeting, the meeting relevance model being trained to produce, asoutput, data identifying a second meeting and a measure of confidencethat the second meeting is relevant to the first meeting, the secondmeeting having previously occurred; determine that the second meeting isrelevant to the first meeting based on the output of the meetingrelevance model; and provide, to a user device associated with the firstmeeting, at least a portion of second meeting data associated with thesecond meeting.

According to some implementations, a non-transitory computer-readablemedium may store instructions, the instructions comprising: one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: receive first meeting data associated with afirst meeting, the first meeting having previously occurred; obtain,based on the first meeting data, data identifying at least one topicassociated with the first meeting; receive second meeting dataassociated with a second meeting, the second meeting having previouslyoccurred; obtain, based on the second meeting data, data identifying atleast one topic associated with the second meeting; determine, based onthe at least one topic associated with the first meeting and the atleast one topic associated with the second meeting, that the firstmeeting is relevant to the second meeting; and provide, to a user deviceassociated with the first meeting, at least a portion of the secondmeeting data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for managing meetingdata.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Meetings frequently take place among a variety of individuals associatedwith a variety of organizations and associated with a variety of topics.Often, an individual uses software to set up a meeting; the individualsetting up the meeting is typically responsible for inviting others tothe meeting, creating the meeting time, describing the meeting, and soon. However, the individual setting up a meeting may not be aware ofother, similar meetings, between the same or similar individuals or onthe same or similar topics. In this situation, the individual setting upthe meeting may neglect to invite individuals that would be valuable toinvite to the meeting, and the meeting may be set up with incompleteinformation that might have been useful to know or include from previousmeetings, or from previously scheduled meetings.

Some implementations, described herein, provide a meeting datamanagement platform that is capable of managing meeting data in a mannerdesigned to facilitate scheduling meetings and sharing informationregarding meetings based on information regarding previous (and/orpreviously scheduled) meetings. For example, a meeting data managementplatform may receive meeting data associated with a meeting thatpreviously occurred. The meeting data may include a variety ofinformation regarding the meeting, including information identifying theindividuals associated with the meeting and information identifying oneor more topics associated with the meeting. The meeting data may bestored in a data structure and used to perform a variety of actions. Forexample, the meeting data management platform may determine that a newmeeting, which has yet to occur, is related to a previously occurringmeeting. The determination may be made, for example, based onsimilarities between individuals and/or topics associated with thepreviously occurring meeting and the new meeting. After identifyingmeetings that are related, the meeting data management platform mayperform a variety of actions, such as suggesting that an individualassociated with the previously occurring meeting be invited to the newmeeting, providing an organizer of the new meeting with a transcript ofthe previously occurring meeting, providing information regarding thenew meeting to an individual associated with the previously occurringmeeting, and/or the like.

In this way, the meeting data management platform may facilitate themanagement of meeting data in a manner designed to increase the ease andefficiency of scheduling meetings and increase awareness betweenindividuals regarding related meetings. For example, scheduling meetingsmay be made easier and more efficient than traditional manual meetingscheduling by suggesting individuals to be added to a meeting and/orsuggesting content from previous meetings that can be included in a newmeeting. Increased awareness between individuals may be provided bysharing information related to meetings between meetings and individualsassociated with the meetings. Several different stages of the processfor managing meeting data are automated, which can improve speed andefficiency of the process and conserve computing resources (e.g.,processor resources, memory resources, and/or the like). Furthermore,implementations described herein use a rigorous, computerized process toperform tasks or roles that were not previously performed. Also,automating the process for managing meeting data conserves computingresources (e.g., processor resources, memory resources, and/or the like)that would otherwise be wasted by using manual processes for attemptingto determine individuals who should be invited to a meeting, identifycontent relevant to include in a meeting invitation, notify individualsrelated to the meeting, and/or the like.

FIG. 1 is a diagram of an example implementation 100 described herein.As shown in FIG. 1, example implementation 100 includes a meeting datadevice (e.g., a telecommunications device, mobile device, personalcomputer, telecommunications server, and/or the like) that is associatedwith a meeting, such as a computer used by a meeting participant for ameeting, a telecommunications broker server facilitating a meetingbetween multiple devices, and/or the like; a language processing device(e.g., a server computer, a cloud computing platform, and/or the like)designed to process information regarding a meeting and turn theinformation into meaningful meeting data; a meeting data managementplatform (e.g., a server computer, a cloud computing platform, and/orthe like) designed to facilitate the management of meeting data; and auser device (e.g., a personal computer, mobile device, server computer,and/or the like) designed to interact with the meeting data managementplatform in a variety of ways. While the devices of implementation 100are depicted separately, in some implementations, the functionality ofone or more of the devices of implementation 100 can be included inanother device, or can be included in multiple, distributed devices.

As shown by reference number 110, the meeting data device provides rawmeeting data to the language processing device. As noted above, themeeting data device may include a device associated with a meeting, suchas a personal computer used by a meeting participant for a meeting, atelecommunications broker server facilitating a meeting between multipledevices, and/or the like. The language processing device may include adevice designed to process information regarding a meeting (e.g., rawmeeting data) and turn the information into meaningful meeting data.While the language processing device is depicted as being separate fromthe meeting data device and meeting data management platform, in someimplementations, the language processing device may be included in themeeting data device or meeting data management platform.

The raw meeting data may include a variety of information associatedwith a meeting that was previously scheduled, or which previouslyoccurred. For example, the raw meeting data may include a voicerecording associated with the first meeting, a video recordingassociated with the first meeting, a transcript associated with thefirst meeting, a chat log associated with the first meeting, textincluded in a meeting invite associated with the first meeting, dataassociated with at least one attachment associated with the firstmeeting, and/or the like. In some implementations, the raw meeting datamay include information that is designed to be processed by a naturallanguage processing device (e.g., the language processing device) toobtain various features of the raw meeting data, including topicinformation (e.g., where the raw meeting data may be categorized,labeled, or otherwise associated with one or more topics), as describedin further detail below.

As shown by reference number 120, the language processing deviceprocesses the raw meeting data to produce useful meeting data that maybe used to facilitate scheduling meetings and transferring informationbetween individuals. In some implementations, the language processingdevice may use a variety of machine learning techniques, includingnatural language processing techniques, to process the raw meeting data.For example, a speech recognition model may be trained to convert speechfound in audio and video into text. The language processing device mayuse the speech recognition model to generate a transcript of the meetingassociated with the raw meeting data. As another example, a voicerecognition model may be trained (e.g., using previous audio/video datafrom previous meetings) to identify individuals speaking during themeeting. In this situation, a transcript of the meeting may be annotatedwith information identifying the individuals speaking, and the times atwhich the individuals are speaking, in a manner designed to produce anannotated transcript of the meeting. Another example machine learningtechnique may include training and using a topic model to identify oneor more topics that are relevant to the meeting and/or the individualsassociated with the meeting. In this situation, the language processingdevice may use text-based data associated with the meeting (e.g.,including a transcript) to label the meeting as being relevant to one ormore topics, and/or labeling individuals associated with the meeting asbeing relevant to one or more topics. Whether the meeting or anindividual is labeled as being relevant to a particular topic may bebased on any of the raw meeting data or meeting data that has beenprocessed by the language processing device.

In some implementations, metadata associated with the meeting may beused by the language processing device to facilitate the training and/oruse of a variety of machine learning models. For example, the rawmeeting data may include or otherwise be associated with dataidentifying the individuals invited to and/or participating in themeeting (e.g., identified by user identifiers, names, job position,phone numbers, MAC addresses, and/or the like). In this situation, thedata identifying the individuals invited to and/or participating in themeeting may be used to increase the accuracy of the voice recognitionmodel (e.g., by limiting the number of voices that the languageprocessing device may recognize for the meeting).

As another example, the raw meeting data may include or otherwise beassociated with data identifying a role, title, and/or the like,associated with the individuals invited to and/or participating in themeeting. In this situation, the data identifying roles, titles, and/orthe like, may be used to increase the accuracy of a topic identificationand/or labeling model (e.g., by weighting topics in a manner designed toincrease the likelihood that an individual will be labeled with a topicthat is associated with the individual's role and/or title). As anotherexample, the raw meeting data may include or otherwise be associatedwith data identifying a conference room or other meeting location orsoftware associated with the meeting. In this situation, the dataidentifying the conference room or other meeting location or softwaremay be used to schedule a conference room, identify software to be usedfor a meeting, order food for a meeting, have products delivered to themeeting, and/or the like. As another example, information regarding howoften an individual actively participated (e.g., presented or spoke) inthe meeting may affect whether the individual is labeled (and/or aconfidence score for the label) as being associated with a topic.

A variety of other types of raw meeting data may be used to identify oneor more topics associated with the meeting and/or individuals associatedwith the meeting. For example, data indicating whether an individualattended a meeting in person or remotely may affect the likelihood ofthe individual being labeled as associated with a particular topic. Inanother example, data identifying a job title associated with theindividuals attending the meeting may be used to identify one or moretopics to be associated with the meeting and/or the individuals.

In some implementations, the language processing device may derive text(e.g., for use in topic identification), from other types of dataassociated with the meeting, and which may be included in or otherwiseassociated with the raw meeting data. For example, the languageprocessing device may derive text from an attachment associated with themeeting, such as a digital document or presentation, and use the text inone or more models for identifying one or more topics to be associatedwith the meeting and/or one or more individuals associated with themeeting. As another example, text included in a meeting invitation,including the subject line and text associated with the meetinginvitation, may be used by the language processing device when using oneor more of the example models described above.

In some implementations, the language processing device may produce, asoutput from one of the machine learning models applied to the rawmeeting data, confidence scores that indicate a measure of confidenceassociated with the machine learning model output. For example, eachtopic produced by a topic identification model may be associated with ameasure of confidence that the topic identification model correctlylabeled a meeting or individual as being relevant to the topic. In asituation where topics are associated with confidence scores, theconfidence scores may be used in a variety of ways. In someimplementations, a confidence score threshold may be used, e.g., bylabeling the meeting or an individual as relevant to a particular topiconly in a situation where the confidence score associated with the labelmeets a threshold measure of confidence. In some implementations,confidence scores may be used to limit the number of topics identifiedas being associated with the meeting or an individual (e.g., by limitingthe number of topics to the N topics with the highest measures ofconfidence, where N is a positive integer).

In this way, the language processing device may process the raw meetingdata to obtain a transcript associated with the meeting, identify one ormore topics associated with the meeting, and/or identify one or moretopics associated with one or more individuals associated with themeeting. The ability to process raw meeting data may be useful toprovide the meeting data management platform with meeting data that canbe used to facilitate the management of meeting data for the meeting(and for many other meetings).

As shown by reference number 130, the meeting data management platformreceives meeting data from the language processing device. As notedabove, the meeting data may include a variety of information regardingthe meeting. For example, the meeting data may include a voice recordingassociated with the first meeting, a video recording associated with thefirst meeting, a transcript associated with the first meeting, dataidentifying individuals invited to the first meeting, data identifyingindividuals attending the first meeting, data identifying rolesassociated with the individuals invited to the first meeting (e.g., therole within the meeting and/or an organization), data identifying rolesassociated with the individuals attending the first meeting, a subjectassociated with the first meeting, text included in a meeting inviteassociated with the first meeting, one or more topics associated withthe first meeting, one or more topics associated with one or moreindividuals associated with the meeting, data associated with at leastone attachment associated with the first meeting, data identifying alocation of the first meeting, data identifying individuals attendingthe first meeting in person, data identifying individuals attending thefirst meeting remotely, software used to conduct the first meeting,products and/or services associated with the first meeting (e.g., foodserved, equipment reserved, and/or the like), data identifying a jobtitle associated with the individuals attending the first meeting,and/or the like. In some implementations, the meeting data is text-based(e.g., the meeting data does not include audio or video but includestext, such as a transcript).

In some implementations, the meeting data specifies one or more topicsassociated with the meeting, and the meeting data may include a measureof confidence associated with each of the one or more topics associatedwith the meeting. In some implementations, the meeting data specifies,for at least one individual associated with the meeting, one or moretopics associated with the individual, and the meeting data may includea measure of confidence associated with each of the one or more topicsassociated with the individual.

In some implementations, the meeting data management platform mayreceive the meeting data based on a request for the meeting data. Forexample, in some implementations, the meeting data platform may receivethe raw meeting data (e.g., from the meeting data device, the userdevice, or a different device). In this situation, the meeting datamanagement platform may send a request to the language processingdevice, to cause the language processing device to process the rawmeeting data and provide the meeting data management platform withmeeting data.

The meeting data received by the meeting data management platformenables the meeting data management platform to perform a variety ofactions related to meeting data management.

As shown by reference number 140, the meeting data management platformobtains data identifying one or more individuals associated with themeeting. As noted above, the meeting data may include data identifyingone or more individuals associated with the meeting. For example, theone or more individuals may include the meeting organizer, individualsinvited to the meeting, individuals attending the meeting, and/or thelike. Individuals associated with the meeting may be identified in avariety of ways, such as by name, user name, job title, user accountidentifier, phone number, computer IP address, and/or the like.

In some implementations, the meeting data management platform may storedata identifying the individuals associated with the meeting. The datamay be stored in a data structure, such as a database, list, table,and/or the like, in a manner designed to associate individuals with themeetings the individuals are, or were, associated with. In someimplementations, an individual may be identified in multiple differentways. For example, in one meeting, an individual may use computersoftware that identifies the user account associated with the user; inanother meeting, the individual may call in using a telephone that isassociated with a phone number. When storing data regarding anindividual, each individual may be associated with multiple methods ofidentification, enabling the meeting data management platform to storedata regarding individuals in a manner designed to enable identifying anindividual from any method of identification included in the meetingdata.

As shown by reference number 150, the meeting data management platformobtains data identifying one or more topics associated with the meeting.As noted above, the meeting data may include data identifying one ormore topics associated with the meeting and/or the individualsassociated with the meeting. For example, the meeting may be identifiedas being associated with multiple topics (e.g., based on the transcriptof the meeting, attachments associated with the meeting, the individualsassociated with the meeting, and/or the like). As another example,individuals associated with the meeting may each be associated with oneor more topics (e.g., based on the individual's involvement in themeeting, portions of the transcript attributable to the individual, theindividual's role or title within an organization, and/or the like).

In some implementations, the meeting data management platform may storedata specifying the topics associated with the meeting and/or the topicsassociated with the individuals associated with the meeting. The datamay be stored in a data structure, such as a database, list, table,and/or the like, in a manner designed to associate topics with themeeting and the individuals associated with the meeting. In someimplementations, the data structure may be the same data structuredescribed above as storing data identifying the individuals associatedwith the meeting. In a situation where confidence scores are providedfor topics identified by the language processing device, the meetingdata management platform may also store, for each topic, a measure ofconfidence that the topic is associated with the meeting and/orindividual.

In some implementations, the meeting data management platform may storemeeting data and/or data specifying the topics associated with themeeting and/or the topics associated with the individuals associatedwith the meeting in a manner designed to enable searching for dataregarding the meeting, an individual associated with the meeting, and/ora topic associated with the meeting. In some implementations, theforegoing data may be stored in a data structure along with similar datafor many other meetings, individuals, and/or topics. In this way, themeeting data management platform may provide the ability to search formeetings and/or individuals related to a particular topic, search fortopics related to an individual and/or meeting, and/or the like. Forexample, in a situation where the foregoing data is stored in a datastructure, a user may query the data structure for a particular topic,and the meeting data management platform may provide meetings andindividuals associated with the particular topic in response to thequery. In implementations where measures of confidence are used, searchresults may be ordered based on the measure of confidence (e.g., whensearching for a particular topic, individuals and meetings with highmeasures of relevance to the searched topic may be ranked higher thanindividuals and meetings with relatively low measures of relevance tothe searched topic).

In some implementations, the meeting data management platform may securemeeting data (including data specifying topics that are relevant toparticular meetings and/or individuals) in a manner designed to restrictaccess to meeting data and data associating topics with individualsand/or meetings. For example, user account controls may be used, whereuser accounts are permitted access to particular subsets of data (e.g.,a user account associated with a particular organization may be able toaccess data regarding meetings and individuals within the particularorganization). Other forms of security, including encryption,multi-factor authentication, and/or the like, may also be used, in someimplementations, to secure access to meeting data stored by the meetingdata management platform.

As shown by reference number 160, the meeting data management platformidentifies a related meeting based on the one or more individuals and/ortopics associated with the meeting (the “first” meeting). As describedin further detail, below, the meeting data management platformidentifies a related meeting to enable the performance of a variety ofactions associated with the related meeting. In some implementations,the related meeting may be a meeting that previously occurred. In someimplementations, the related meeting may be a meeting that has not yetoccurred (e.g., a meeting that is being scheduled or has beenscheduled). The related meeting may be identified in a variety of ways.

In some implementations, the related meeting may be identified (e.g., asbeing related to first meeting) based on one or more topics associatedwith the related meeting. As noted above, the first meeting may beassociated with multiple topics (e.g., including the topics that areassociated with the individuals associated with the first meeting). In asituation where the related meeting is also associated with one or moretopics, the related meeting may be identified based on similaritiesbetween the topics. For example, the related meeting may be identified(e.g., identified as being related to the first meeting) based on therelated meeting's association with topics that are similar to or thesame as one or more of the topics associated with the first meeting. Insome implementations, one or more thresholds may be used to determinewhether a meeting is related to the first meeting. For example, ameeting may be identified as related in a situation where a thresholdnumber of topics associated with the first meeting match the meeting.

In a situation where confidence scores are used to indicate a measure ofconfidence that topics are associated with meetings, the measures ofconfidence may also be used to identify the related meeting. Forexample, the related meeting may be identified as related in a situationwhere the measures of confidence for the topics associated with therelated meeting meet or exceed a threshold measure of relevance (e.g.,for the topics that match between the first meeting and the relatedmeeting).

In some implementations, the related meeting may be identified frommultiple meetings. For example, in a situation where the meeting datamanagement platform stores meeting data (e.g., including associatedtopics) for multiple meetings, multiple meetings may be associated withtopics that are similar to or the same as the topics associated with thefirst meeting. In this situation, the meeting data management platformmay identify, as the related meeting, the most relevant meeting (e.g.,based on similar and/or matching topics, and relevance scores). In someimplementations, the meeting data management platform may identifymultiple meetings as related meetings. For example, the meeting datamanagement platform may identify, as related meetings, a predeterminednumber of the most relevant meetings. As another example, the meetingdata management platform may identify, as related meetings, each meetingthat exceeds a threshold measure of relevance associated to the firstmeeting.

In some implementations, the meeting data management platform mayidentify the related meeting by using one or more machine learningtechniques. For example, the meeting data management platform may trainand use a meeting relevance model to determine which meetings arerelated to one another (and, in some implementations, a degree to whichthe meetings are related). The meeting data management platform maytrain the meeting relevance model using one or more supervised machinelearning techniques and based on training data that includes informationidentifying previous meetings and determinations regarding whichmeetings are relevant to one another.

The meeting data management platform may use a variety of informationincluded in meeting data as machine learning features to be used intraining and using the meeting relevance model. For example, the machinelearning features may include one or more of: data identifyingindividuals invited to the meeting, data identifying individualsattending the meeting, data identifying roles associated with theindividuals invited to the meeting, data identifying roles associatedwith the individuals attending the meeting, a subject associated withthe meeting, at least one topic associated with the meeting (includingtopics associated with the meeting and/or the individuals associatedwith the meeting), data identifying at least one attachment associatedwith the meeting, and/or the like.

The meeting relevance model may be trained to receive meeting dataassociated with a meeting as input and produce, as output, dataidentifying a second meeting and a measure of confidence that the secondmeeting is relevant to the first meeting. In some implementations, themeeting relevance model may produce data identifying multiple meetingsas output, with corresponding measures of confidence for each meetingproduced as output. Based on the output of the meeting relevance model,including the measures of confidence, the meeting data managementplatform may identify one or more meetings as being related to the firstmeeting.

The ability to identify a meeting as being related to the first meetingmay enable the meeting data management platform to perform a variety ofactions designed to facilitate meeting scheduling and/or the sharing ofknowledge between individuals.

As shown by reference number 170, the meeting data management platformperforms an action based on the meeting data associated with the relatedmeeting. As noted above, the meeting data management platform mayperform a variety of actions designed to facilitate meeting schedulingand/or the sharing of knowledge between individuals. For example, and asdescribed in further detail below, the meeting data management platformmay suggest individuals and/or content to be added to a meeting that hasnot yet occurred; automatically provide meeting information for apreviously occurring meeting to an individual related to topicassociated with the meeting, automatically schedule a meeting room for ameeting, and/or the like.

In some implementations, the meeting data management platform mayprovide at least a portion of the meeting data to a user deviceassociated with an individual associated with the related meeting. Themeeting data provided to the user device may be based on the meetingdata used to identify the related meeting (e.g., data related to topicsand/or individuals). For example, in a situation where the relatedmeeting has not yet occurred (e.g., the meeting is being scheduled orwas previously scheduled), the meeting data management platform mayprovide, to a user device associated with the organizer of the relatedmeeting, meeting data associated with the first meeting that might berelevant to include in the related meeting. The meeting data provided tothe user device may include suggested individuals (e.g., invitees orattendees of the first meeting) to invite to the related meeting, atranscript of the first meeting, an attachment associated with the firstmeeting, a suggested subject for the meeting invitation, a suggestedconference room to schedule, and/or the like.

In some implementations, the meeting data management platform mayprovide at least a portion of meeting data associated with the relatedmeeting to a user device associated with an individual associated withthe first meeting. For example, in a situation where the related meetinghas not yet occurred, the meeting data management platform may provide,to a user device associated with an individual (e.g., an individualassociated with one or more topics), meeting data that might be relevantto provide to the individual. For example, the meeting data provided tothe user device, in this situation, may include data notifying theindividual that a meeting relevant to the individual is scheduled (or isbeing scheduled), data identifying the meeting organizer associated withthe related meeting, and/or other information which the meetingorganizer may have agreed to share with individuals relevant to therelated meeting.

In some implementations, the meeting data management platform mayprovide meeting data to a user device during a meeting and/or performanother action during a meeting. For example, during a meeting, themeeting data management platform may receive meeting data from a meetingdata device and identify a related meeting based on the meeting dataprovided during the meeting. The meeting data may be provided, duringthe meeting, to a user device associated with an individualparticipating in the meeting, thus enabling the individual using theuser device to take action based on the meeting data. By way of example,during a meeting, a discussion regarding a particular person or productmay arise. Based on a transcript of the meeting (e.g., generated duringthe meeting), the meeting data management platform may determine thatthe meeting (which was previously not relevant to the particular personor product) is relevant to the particular person or product. In asituation where the meeting is identified as being relevant to theparticular person, the organizer of the meeting may be prompted toinvite the particular person to the meeting, enabling the particularperson to join the meeting in progress in response to the particularperson becoming relevant during the meeting. In a situation where themeeting is identified as being relevant to the product, the meeting datamanagement platform may provide, to the individuals participating in themeeting, relevant meeting data (e.g., attachments, transcripts, and/orthe like) regarding the product from a related meeting.

In some implementations, the meeting data management platform mayschedule a new meeting between at least one individual associated withthe first meeting and at least one individual associated with therelated meeting. For example, based on the meeting data associated withthe first meeting and/or the related meeting, the meeting datamanagement platform may determine (e.g., based on meeting transcripts)that a third meeting is desirable between at least one individualassociated with the first meeting and at least one individual associatedwith the related meeting. In this situation, the meeting data managementplatform may schedule a meeting, including scheduling a conference room(if applicable), and automatically send meeting invites to theindividuals identified as relevant to the new meeting (e.g., based ontopics associated with the individuals and/or the individuals'involvement in the first meeting and/or relevant meeting). Whenscheduling a meeting, the meeting data management platform may use othermeeting data to take additional actions, such as using a previoustranscript to determine food preference for the first meeting andautomatically order food for the new meeting, determine equipmentpreviously reserved for the meeting or similar meetings andautomatically reserve equipment for the new meeting, and/or the like.

In some implementations, the meeting data management platform mayprovide meeting data as search results to a user device. For example, ina situation where the meeting data management platform stores meetingdata and enables individuals to search for meeting data and/orindividuals that are relevant to a particular topic, the meeting datamanagement platform may receive a query for a particular topic, identifymeeting data related to the topic, and provide search results thatinclude at least a portion of the meeting data related to the topic.

In some implementations, the meeting data management platform may usevarious security measures, such as the security measures describedabove, when performing one or more actions. For example, the individualsto which meeting data is provided, and/or the portions of meeting dataprovided, may depend on security settings designed to protectpotentially sensitive meeting data from being provided to unauthorizeand/or unintended recipients. Example security settings include useraccount verification, multi-factor authentication for meeting datasharing, and/or the like.

The ability to perform actions, such as those described above, based onmeeting data, enable the meeting data management platform to managemeeting data in a manner designed to facilitate meeting schedulingand/or the sharing of knowledge between individuals. As a specificexample, an individual working for an organization may schedule ameeting regarding the marketing for the release of a new product thatcompetes with a competing organization's competing product. While theindividual is setting up the meeting, and after providing the meetingdata management platform with enough information (e.g., in the subjector text of the meeting invitation) to identify topics associated withthe meeting (e.g., marketing, the new product, the competingorganization, and the competing product), the meeting data managementplatform may identify a related individual that is relevant to themeeting based on the related individual's previous involvement inmeetings associated with topics similar to marketing, the new product,the competitor, and/or the competitor's product. After identifying therelated individual (e.g., the relevant meeting data), the meeting datamanagement platform may suggest, to the individual, that the relatedindividual should be invited to the meeting. After creation of themeeting, the meeting data management platform may identify a relatedmeeting between other individuals that included a technical analysis ofthe differences between the new product and the competing product. Basedon identifying the related meeting, the meeting data management platformmay send meeting data associated with the related meeting to theindividual setting up the meeting, such as a transcript of the relatedmeeting, documents presented during the related meeting, and/or thelike, which the individual may then choose to distribute to otherindividuals invited to the meeting. After the meeting, the meeting datamay be analyzed and stored, in a manner designed to enable future use ofthe meeting data for scheduling meetings and sharing knowledge betweenindividuals.

By performing one or more actions similar to those described above, themeeting data management platform may conserve both human and computingresources that would otherwise be used to manually identify previousmeetings and/or individuals that might be related to another meeting.The actions may make meeting scheduling and knowledge transfer easierand more efficient than would otherwise be possible, or practicable.

As shown by reference number 180, the meeting data management platformprovides relevant meeting data to the user device. For example, the userdevice of the example implementation 100 may be associated with anindividual associated with the related meeting. The relevant meetingdata may include at least a portion of the meeting data associated withthe first meeting, e.g., in the form a suggested meeting invitee, arelated document that was presented during the first meeting, and/or thelike. In some implementations, the relevant meeting data may be providedby the meeting data management platform in response to a request fromthe user device (e.g., a request for meeting data relevant to a newmeeting, a search query for meeting data related to a particular topic,and/or the like). While a single user device is depicted as receivingrelevant meeting data, in some implementations, multiple user devicesmay receive the relevant meeting data, or at least a portion of therelevant meeting data. For example, in a situation where a relevantdocument is being provided, the meeting data management platform mayprovide the relevant document to multiple users associated with ameeting.

In this way, the meeting data management platform may facilitate themanagement of meeting data in a manner designed to increase the ease andefficiency of scheduling meetings and increase awareness betweenindividuals regarding related meetings. For example, scheduling meetingsmay be made easier and more efficient than traditional manual meetingscheduling by suggesting individuals to be added to a meeting and/orsuggesting content from previous meetings that can be included in a newmeeting. Increased awareness between individuals may be provided bysharing information related to meetings between meetings and individualsassociated with the meetings. Several different stages of the processfor managing meeting data are automated, which can improve speed andefficiency of the process and conserve computing resources (e.g.,processor resources, memory resources, and/or the like). Furthermore,implementations described herein use a rigorous, computerized process toperform tasks or roles that were not previously performed. Also,automating the process for managing meeting data conserves computingresources (e.g., processor resources, memory resources, and/or the like)that would otherwise be wasted by using manual processes for attemptingto determine individuals who should be invited to a meeting, identifycontent relevant to include in a meeting invitation, notify individualsrelated to the meeting, and/or the like.

As indicated above, FIG. 1 is provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIG. 1.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a meeting data device 210, a languageprocessing device 220, a meeting data management platform 230, acomputing resource 235, a cloud computing environment 240, a user device250, and a network 260. Devices of environment 200 may interconnect viawired connections, wireless connections, or a combination of wired andwireless connections.

Meeting data device 210 includes one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationassociated with meetings and/or meeting data. For example, meeting datadevice 210 can include a communication and/or computing device, such asa mobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, server computer, or a similar type ofdevice. Meeting data device 210 may record voice and video dataassociated with a meeting, obtain text associated with a meeting (e.g.,meeting invitation text), obtain metadata associated with a meeting, andbe capable of communicating raw meeting data to one or more otherdevices.

Language processing device 220 includes one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationassociated with meeting data. For example, language processing devicemay include a server device (e.g., a host server, a web server, anapplication server, etc.), a data center device, or a similar device.Language processing device 220 may be capable of processing raw meetingdata, including meeting audio and/or video, to obtain text-based meetingdata that can be provided to meeting data management platform 230.

Meeting data management platform 230 includes one or more devicescapable of receiving, generating, storing, processing, and/or providinginformation associated with meeting data. For example, meeting datamanagement platform 230 may include a server device (e.g., a hostserver, a web server, an application server, etc.), a data centerdevice, or a similar device. Meeting data management platform 230 may becapable of identifying meetings that are related to one another based onindividuals and/or topics associated with the meetings and/or theindividuals associated with the meetings. Based on meeting dataassociated with related meetings, meeting data management platform 230may perform a variety of actions designed to facilitate the schedulingof meetings and/or sharing of information between individuals associatedwith meetings.

In some implementations, as shown, meeting data management platform 230may be hosted in cloud computing environment 240. Notably, whileimplementations described herein describe meeting data managementplatform 230 as being hosted in cloud computing environment 240, in someimplementations, meeting data management platform 230 may not becloud-based (i.e., can be implemented outside of a cloud computingenvironment 240) or may be partially cloud-based.

Cloud computing environment 240 includes an environment that deliverscomputing as a service, whereby shared resources, services, etc. may beprovided to meeting data management platform 230. Cloud computingenvironment 240 may provide computation, software, data access, storage,and/or other services that do not require end-user knowledge of aphysical location and configuration of a system and/or a device thatdelivers the services. As shown, cloud computing environment 240 mayinclude meeting data management platform 230 and computing resource 235.

Computing resource 235 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource235 may host meeting data management platform 230. The cloud resourcesmay include compute instances executing in computing resource 235,storage devices provided in computing resource 235, data transferdevices provided by computing resource 235, etc. In someimplementations, computing resource 235 may communicate with othercomputing resources 235 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 2, computing resource 235 may include a groupof cloud resources, such as one or more applications (“APPs”) 235-1, oneor more virtual machines (“VMs”) 235-2, virtualized storage (“VSs”)235-3, one or more hypervisors (“HYPs”) 235-4, or the like.

Application 235-1 includes one or more software applications that may beprovided to or accessed by user device 250 and/or meeting datamanagement platform 230. Application 235-1 may eliminate a need toinstall and execute the software applications on user device 250 and/ormeeting data management platform 230. For example, application 235-1 mayinclude software associated with meeting data management platform 230and/or any other software capable of being provided via cloud computingenvironment 240. In some implementations, one application 235-1 maysend/receive information to/from one or more other applications 235-1,via virtual machine 235-2.

Virtual machine 235-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 235-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 235-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 235-2 may execute on behalf of a user(e.g., user device 250), and may manage infrastructure of cloudcomputing environment 240, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 235-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 235. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 235-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 235.Hypervisor 235-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

User device 250 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith meeting data. For example, user device 250 may include acommunication and/or computing device, such as a mobile phone (e.g., asmart phone, a radiotelephone, etc.), a laptop computer, a tabletcomputer, a handheld computer, a gaming device, a wearable communicationdevice (e.g., a smart wristwatch, a pair of smart eyeglasses, etc.), ora similar type of device. User device 250 may be capable of providing arequest for meeting data relevant to a related meeting, topic, and/orindividual, and receiving relevant meeting data (e.g., from meeting datamanagement platform 230).

Network 260 includes one or more wired and/or wireless networks. Forexample, network 260 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, etc.), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the Public Switched TelephoneNetwork (PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to meeting data device 210, language processing device220, meeting data management platform 230, computing resource 235,and/or user device 250. In some implementations, meeting data device210, language processing device 220, meeting data management platform230, computing resource 235, and/or user device 250 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3, device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for managing meetingdata. In some implementations, one or more process blocks of FIG. 4 maybe performed by a meeting data management platform (e.g., meeting datamanagement platform 230). In some implementations, one or more processblocks of FIG. 4 may be performed by another device or a group ofdevices separate from or including the meeting data management platform,such as a meeting data device (e.g., meeting data device 210), alanguage processing device (e.g., language processing device 220), acomputing resource (e.g., computing resource 235), and/or a user device(e.g., user device 250).

As shown in FIG. 4, process 400 may include receiving meeting dataassociated with a first meeting, the first meeting having previouslyoccurred (block 410). For example, the meeting data management platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, input component 350, communications interface 370, and/orthe like) may receive meeting data associated with a first meeting, asdescribed above in connection with FIG. 1. In some implementations, thefirst meeting previously occurred.

As further shown in FIG. 4, process 400 may include obtaining, based onthe meeting data, data identifying at least one individual associatedwith the first meeting (block 420). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationsinterface 370, and/or the like) may obtain, based on the meeting data,data identifying at least one individual associated with the firstmeeting, as described above in connection with FIG. 1.

As further shown in FIG. 4, process 400 may include obtaining, based onthe meeting data, data identifying at least one topic associated withthe first meeting (block 430). For example, the meeting data managementplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, input component 350, communications interface370, and/or the like) may obtain, based on the meeting data, dataidentifying at least one topic associated with the first meeting, asdescribed above in connection with FIG. 1.

As further shown in FIG. 4, process 400 may include identifying a secondmeeting based on the at least one individual or the at least one topic,the second meeting having not yet occurred (block 440). For example, themeeting data management platform (e.g., using computing resource 235,processor 320, memory 330, storage component 340, input component 350,output component 360, communications interface 370, and/or the like) mayidentify a second meeting based on the at least one individual or the atleast one topic, as described above in connection with FIG. 1. In someimplementations, the second meeting did not yet occur.

As further shown in FIG. 4, process 400 may include providing, to a userdevice associated with the second meeting and based on identifying thesecond meeting, at least a portion of the meeting data associated withthe first meeting (block 450). For example, the meeting data managementplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, output component 360, communications interface370, and/or the like) may provide, to a user device associated with thesecond meeting and based on identifying the second meeting, at least aportion of the meeting data associated with the first meeting, asdescribed above in connection with FIG. 1.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described below.

In some implementations, the meeting data includes at least one of: avoice recording associated with the first meeting, a video recordingassociated with the first meeting, a transcript associated with thefirst meeting, data identifying individuals invited to the firstmeeting, data identifying individuals attending the first meeting, dataidentifying roles associated with the individuals invited to the firstmeeting, data identifying roles associated with the individualsattending the first meeting, a subject associated with the firstmeeting, text associated with a meeting invite associated with the firstmeeting, the at least one topic associated with the first meeting, ordata associated with at least one attachment associated with the firstmeeting.

In some implementations, the meeting data was received from a meetingdata device associated with the first meeting. In some implementations,process 400 may include storing, in a data structure, data specifyingthat the first meeting and the second meeting are relevant to oneanother. The data specifying that the first meeting and the secondmeeting are relevant to one another includes at least a portion of themeeting data associated with the first meeting and at least a portion ofmeeting data associated with the second meeting.

In some implementations, the data identifying at least one individualassociated with the first meeting includes data identifying, for atleast one of the at least one individual, data identifying anorganizational role associated with the individual. In someimplementations, identifying the second meeting based on the at leastone individual or the at least one topic comprises: determining that anorganizational role, associated with a particular individual included inthe at least one individual associated with the first meeting, isrelevant to a topic associated with the second meeting, and identifyingthe second meeting based on the determination that the organizationalrole, associated with the particular individual included in the at leastone individual associated with the first meeting, is relevant to thetopic associated with the second meeting. In some implementations,providing, to the user device associated with the second meeting, atleast a portion of the meeting data associated with the first meetingcomprises: providing, to the user device associated with the secondmeeting, data identifying the particular individual.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for managing meetingdata. In some implementations, one or more process blocks of FIG. 5 maybe performed by a meeting data management platform (e.g., meeting datamanagement platform 230). In some implementations, one or more processblocks of FIG. 5 may be performed by another device or a group ofdevices separate from or including the meeting data management platform,such as a meeting data device (e.g., meeting data device 210), alanguage processing device (e.g., language processing device 220), acomputing resource (e.g., computing resource 235), and/or a user device(e.g., user device 250).

As shown in FIG. 5, process 500 may include receiving first meeting dataassociated with a first meeting, the first meeting having not yetoccurred (block 510). For example, the meeting data management platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, input component 350, communications interface 370, and/orthe like) may receive first meeting data associated with a firstmeeting, as described above in connection with FIG. 1. In someimplementations, the first meeting did not yet occur.

As further shown in FIG. 5, process 500 may include obtaining, based onthe first meeting data, data identifying at least one individualassociated with the first meeting (block 520). For example, the meetingdata management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, input component 350,communications interface 370, and/or the like) may obtain, based on thefirst meeting data, data identifying at least one individual associatedwith the first meeting, as described above in connection with FIG. 1.

As further shown in FIG. 5, process 500 may include obtaining, based onthe first meeting data, data identifying at least one topic associatedwith the first meeting (block 530). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationsinterface 370, and/or the like) may obtain, based on the first meetingdata, data identifying at least one topic associated with the firstmeeting, as described above in connection with FIG. 1.

As further shown in FIG. 5, process 500 may include providing, to ameeting relevance model, the data identifying the at least oneindividual associated with the first meeting and the data identifying atleast one topic associated with the first meeting, the meeting relevancemodel being trained to produce, as output, data identifying a secondmeeting and a measure of confidence that the second meeting is relevantto the first meeting, the second meeting having previously occurred(block 540). For example, the meeting data management platform (e.g.,using computing resource 235, processor 320, memory 330, storagecomponent 340, output component 360, communications interface 370,and/or the like) may provide to a meeting relevance model, the dataidentifying the at least one individual associated with the firstmeeting and the data identifying at least one topic associated with thefirst meeting, as described above in connection with FIG. 1. In someimplementations, the meeting relevance model has been trained toproduce, as output, data identifying a second meeting and a measure ofconfidence that the second meeting is relevant to the first meeting. Insome implementations, the second meeting previously occurred.

As further shown in FIG. 5, process 500 may include determining that thesecond meeting is relevant to the first meeting based on the output ofthe meeting relevance model (block 550). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, and/or the like) may determine thatthe second meeting is relevant to the first meeting based on the outputof the meeting relevance model, as described above in connection withFIG. 1.

As further shown in FIG. 5, process 500 may include providing, to a userdevice associated with the first meeting, at least a portion of secondmeeting data associated with the second meeting (block 560). Forexample, the meeting data management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, outputcomponent 360, communications interface 370, and/or the like) mayprovide to a user device associated with the first meeting, at least aportion of second meeting data associated with the second meeting, asdescribed above in connection with FIG. 1.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described below.

In some implementations, the first meeting data includes at least oneof: data identifying one or more individuals invited to the firstmeeting, data identifying roles associated with the one or moreindividuals invited to the first meeting, a subject associated with thefirst meeting, text included in a meeting invite associated with thefirst meeting, the at least one topic associated with the first meeting,or data associated with at least one attachment associated with thefirst meeting.

In some implementations, process 500, when determining that the secondmeeting is relevant to the first meeting based on the output of themeeting relevance model, is to determine that the at least one topicassociated with the first meeting is relevant to at least one topicassociated with the second meeting and determine that the second meetingis relevant to the first meeting based on the determination that the atleast one topic associated with the first meeting is relevant to the atleast one topic associated with the second meeting.

In some implementations, process 500, when determining that the secondmeeting is relevant to the first meeting based on the output of themeeting relevance model, is to: determine that the at least one topicassociated with the first meeting is relevant to at least one individualassociated with the second meeting and determine that the second meetingis relevant to the first meeting based on the determination that the atleast one topic associated with the first meeting is relevant to the atleast one individual associated with the second meeting.

In some implementations, process 500, when determining that the at leastone topic associated with the first meeting is relevant to at least oneindividual associated with the second meeting, is to: identify, for aparticular individual associated with the second meeting, a particulartopic that is relevant to the particular individual and determine thatthe particular topic matches the at least one topic associated with thefirst meeting. In some implementations, the second meeting is identifiedfrom multiple meetings that were provided, as output, by the meetingrelevance model.

In some implementations, the portion of the second meeting dataassociated with the second meeting includes data identifying the atleast one individual associated with the second meeting.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for managing meetingdata. In some implementations, one or more process blocks of FIG. 6 maybe performed by a meeting data management platform (e.g., meeting datamanagement platform 230). In some implementations, one or more processblocks of FIG. 6 may be performed by another device or a group ofdevices separate from or including the meeting data management platform,such as a meeting data device (e.g., meeting data device 210), alanguage processing device (e.g., language processing device 220), acomputing resource (e.g., computing resource 235), and/or a user device(e.g., user device 250).

As shown in FIG. 6, process 600 may include receiving first meeting dataassociated with a first meeting, the first meeting having previouslyoccurred (block 610). For example, the meeting data management platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, input component 350, communications interface 370, and/orthe like) may receive first meeting data associated with a firstmeeting, as described above in connection with FIG. 1. In someimplementations, the first meeting previously occurred.

As further shown in FIG. 6, process 600 may include obtaining, based onthe first meeting data, data identifying at least one topic associatedwith the first meeting (block 620). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationsinterface 370, and/or the like) may obtain, based on the first meetingdata, data identifying at least one topic associated with the firstmeeting, as described above in connection with FIG. 1.

As further shown in FIG. 6, process 600 may include receiving secondmeeting data associated with a second meeting, the second meeting havingpreviously occurred (block 630). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationsinterface 370, and/or the like) may receive second meeting dataassociated with a second meeting, as described above in connection withFIG. 1. In some implementations, the second meeting previously occurred.

As further shown in FIG. 6, process 600 may include obtaining, based onthe second meeting data, data identifying at least one topic associatedwith the second meeting (block 640). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationsinterface 370, and/or the like) may obtain, based on the second meetingdata, data identifying at least one topic associated with the secondmeeting, as described above in connection with FIG. 1.

As further shown in FIG. 6, process 600 may include determining, basedon the at least one topic associated with the first meeting and the atleast one topic associated with the second meeting, that the firstmeeting is relevant to the second meeting (block 650). For example, themeeting data management platform (e.g., using computing resource 235,processor 320, memory 330, storage component 340, and/or the like) maydetermine, based on the at least one topic associated with the firstmeeting and the at least one topic associated with the second meeting,that the first meeting is relevant to the second meeting, as describedabove in connection with FIG. 1.

As further shown in FIG. 6, process 600 may include providing, to a userdevice associated with the first meeting, at least a portion of thesecond meeting data (block 660). For example, the meeting datamanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, output component 360, communicationsinterface 370, and/or the like) may provide to a user device associatedwith the first meeting, at least a portion of second meeting data, asdescribed above in connection with FIG. 1.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described below.

In some implementations, the at least one topic associated with thefirst meeting was derived from a first transcript associated with thefirst meeting and the at least one topic associated with the secondmeeting was derived from a second transcript associated with the secondmeeting.

In some implementations, the at least one topic associated with thefirst meeting was derived from a first subject associated with the firstmeeting and the at least one topic associated with the second meetingwas derived from a second subject associated with the second meeting.

In some implementations, the at least one topic associated with thefirst meeting was derived from text included in a meeting invitationassociated with the first meeting and the at least one topic associatedwith the second meeting was derived from text included in a meetinginvitation associated with the second meeting.

In some implementations, process 600 may include providing the firstmeeting data to a language processing device; and providing the secondmeeting data to the language processing device. The data identifying theat least one topic associated with the first meeting is obtained fromthe language processing device, and the data identifying the at leastone topic associated with the second meeting is obtained from thelanguage processing device.

In some implementations, the portion of the second meeting data isincluded in data suggesting a third meeting, the data suggesting thethird meeting including data identifying at least one individualassociated with the first meeting and at least one individual associatedwith the second meeting.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

In this way, meeting data management platform 230 may facilitate themanagement of meeting data in a manner designed to increase the ease andefficiency of scheduling meetings and increase awareness betweenindividuals regarding related meetings. For example, scheduling meetingsmay be made easier and more efficient than traditional manual meetingscheduling by suggesting individuals to be added to a meeting and/orsuggesting content from previous meetings that can be included in a newmeeting. Increased awareness between individuals may be provided bysharing information related to meetings between meetings and individualsassociated with the meetings. Several different stages of the processfor managing meeting data are automated, which can improve speed andefficiency of the process and conserve computing resources (e.g.,processor resources, memory resources, and/or the like). Furthermore,implementations described herein use a rigorous, computerized process toperform tasks or roles that were not previously performed. Also,automating the process for managing meeting data conserves computingresources (e.g., processor resources, memory resources, and/or the like)that would otherwise be wasted by using manual processes for attemptingto determine individuals who should be invited to a meeting, identifycontent relevant to include in a meeting invitation, notify individualsrelated to the meeting, and/or the like.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, or the like.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: obtaining, by a device,meeting data associated with a first meeting; providing, by the deviceand to a meeting relevance model, the meeting data, the meetingrelevance model being trained to produce, as output, data identifying asecond meeting and a measure of confidence that the second meeting isrelevant to the first meeting; determining, by the device, that thesecond meeting is relevant to the first meeting based on the output ofthe meeting relevance model; storing, by the device, at least a portionof the meeting data associated with the first meeting in associationwith second meeting data associated with the second meeting to allowaccess to the portion of the meeting data associated with the firstmeeting by accessing the second meeting data associated with the secondmeeting; and providing, by the device and to a user device associatedwith the second meeting, the portion of the meeting data associated withthe first meeting.
 2. The method of claim 1, wherein the meeting dataincludes at least one of: information identifying a subject associatedwith the first meeting, information identifying an individual invited tothe first meeting, information identifying an individual attending thefirst meeting, information identifying one or more roles associated withan individual invited to the first meeting, information identifying oneor more roles associated with an individual attending the first meeting,information identifying one or more topics associated with the firstmeeting, a recording associated with the first meeting, a transcriptassociated with the first meeting, text associated with a meeting inviteassociated with the first meeting, or information associated with anattachment associated with the first meeting.
 3. The method of claim 1,further comprising: determining a third meeting using the portion of themeeting data associated with the first meeting and a portion of thesecond meeting data associated with the second meeting.
 4. The method ofclaim 3, further comprising: determining third meeting data for thethird meeting based on the meeting data.
 5. The method of claim 1,further comprising: determining measures of confidence for a pluralityof other meetings; and determining whether the second meeting is morerelevant to the first meeting than at least one other meeting of theplurality of other meetings based on comparing the measure of confidenceand the measures of confidence for the plurality of other meetings. 6.The method of claim 1, wherein obtaining the meeting data comprises:obtaining the meeting data by performing one or more natural languageprocessing techniques on information associated with the first meeting.7. The method of claim 1, further comprising: identifying a user deviceassociated with the first meeting; identifying user account dataassociated with the user device associated with the first meeting; anddetermining the portion of the meeting data associated with the firstmeeting based on the user account data.
 8. A device, comprising: one ormore memories; and one or more processors communicatively coupled to theone or more memories, configured to: obtain meeting data associated witha first meeting; provide, to a meeting relevance model, the meetingdata, the meeting relevance model being trained to produce, as output,data identifying a second meeting and a measure of confidence that thesecond meeting is relevant to the first meeting; determine that thesecond meeting is relevant to the first meeting based on the output ofthe meeting relevance model; store at least a portion of the meetingdata associated with the first meeting in association with secondmeeting data associated with the second meeting to allow access to theportion of the meeting data associated with the first meeting byaccessing the second meeting data associated with the second meeting;and provide, to a user device associated with the second meeting, theportion of the meeting data associated with the first meeting.
 9. Thedevice of claim 8, wherein the meeting data includes at least one of:information identifying a subject associated with the first meeting,information identifying an individual invited to the first meeting,information identifying an individual attending the first meeting,information identifying one or more roles associated with an individualinvited to the first meeting, information identifying one or more rolesassociated with an individual attending the first meeting, informationidentifying one or more topics associated with the first meeting, arecording associated with the first meeting, a transcript associatedwith the first meeting, text associated with a meeting invite associatedwith the first meeting, or information associated with an attachmentassociated with the first meeting.
 10. The device of claim 8, whereinthe one or more processors are further configured to: determine a thirdmeeting using the portion of the meeting data associated with the firstmeeting and a portion of the second meeting data associated with thesecond meeting.
 11. The device of claim 10, wherein the one or moreprocessors are further configured to: determine third meeting data forthe third meeting based on the meeting data.
 12. The device of claim 8,wherein the one or more processors are further configured to: determinemeasures of confidence for a plurality of other meetings; and determinewhether the second meeting is more relevant to the first meeting than atleast one other meeting of the plurality of other meetings based oncomparing the measure of confidence and the measures of confidence forthe plurality of other meetings.
 13. The device of claim 8, wherein theone or more processors, when obtaining the meeting data, are configuredto: obtain the meeting data by performing one or more natural languageprocessing techniques on information associated with the first meeting.14. The device of claim 8, wherein the one or more processors arefurther configured to: identify a user device associated with the firstmeeting; identify user account data associated with the user deviceassociated with the first meeting; and determine the portion of themeeting data associated with the first meeting based on the user accountdata.
 15. A non-transitory computer-readable medium storinginstructions, the instructions comprising: one or more instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: obtain meeting data associated with a first meeting;provide, to a meeting relevance model, the meeting data, the meetingrelevance model being trained to produce, as output, data identifying asecond meeting and a measure of confidence that the second meeting isrelevant to the first meeting; determine that the second meeting isrelevant to the first meeting based on the output of the meetingrelevance model; store at least a portion of the meeting data associatedwith the first meeting in association with second meeting dataassociated with the second meeting to allow access to the portion of themeeting data associated with the first meeting by accessing the secondmeeting data associated with the second meeting; and provide, to a userdevice associated with the second meeting, the portion of the meetingdata associated with the first meeting.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the meeting data includesat least one of: information identifying a subject associated with thefirst meeting, information identifying an individual invited to thefirst meeting, information identifying an individual attending the firstmeeting, information identifying one or more roles associated with anindividual invited to the first meeting, information identifying one ormore roles associated with an individual attending the first meeting,information identifying one or more topics associated with the firstmeeting, a recording associated with the first meeting, a transcriptassociated with the first meeting, text associated with a meeting inviteassociated with the first meeting, or information associated with anattachment associated with the first meeting.
 17. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: determine a third meeting using theportion of the meeting data associated with the first meeting and aportion of the second meeting data associated with the second meeting.18. The non-transitory computer-readable medium of claim 17, wherein theone or more instructions, when executed by the one or more processors,further cause the one or more processors to: determine third meetingdata for the third meeting based on the meeting data.
 19. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: determine measures of confidencefor a plurality of other meetings; and determine whether the secondmeeting is more relevant to the first meeting than at least one othermeeting of the plurality of other meetings based on comparing themeasure of confidence and the measures of confidence for the pluralityof other meetings.
 20. The non-transitory computer-readable medium ofclaim 15, wherein the one or more instructions, when executed by the oneor more processors, further cause the one or more processors to:identify a user device associated with the first meeting; identify useraccount data associated with the user device associated with the firstmeeting; and determine the portion of the meeting data associated withthe first meeting based on the user account data.